Finding weaknesses in networks using Greedy Randomized Adaptive Search Procedure and Path Relinking

Pérez‐Peló, Sergio; Sánchez‐Oro, Jesús; Duarte, Abraham

doi:10.1111/exsy.12540

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…In particular, we perform 1000 complete GRASP iterations (construction and improvement phase), returning the best solution found during the search. See (Duarte et al 2015;Pérez-Peló et al 2020;Casas-Martínez et al 2021) for recent successful applications of the GRASP metaheuristic in a diverse family of combinatorial optimization problems. Algorithm 1 shows the pseudocode of the GRASP scheme.…”

Section: Algorithmic Proposalmentioning

confidence: 99%

A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem

et al. 2022

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The selection of individuals with similar characteristics from a given population have always been a matter of interest in several scientific areas: data privacy, genetics, art, among others. This work is focused on the maximum intersection of k-subsets problem (kMIS). This problem tries to find a subset of k individuals with the maximum number of features in common from a given population and a set of relevant features. The research presents a Greedy Randomized Adaptive Search Procedure (GRASP) where the local improvement is replaced by a complete Tabu Search metaheuristic with the aim of further improving the quality of the obtained solutions. Additionally, a novel representation of the solution is considered to reduce the computational effort. The experimental comparison carefully analyzes the contribution of each part of the algorithm to the final results as well as performs a thorough comparison with the state-of-the-art method. Results, supported by non-parametric statistical tests, confirms the superiority of the proposal.

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Section: Algorithmic Proposalmentioning

confidence: 99%

A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem

et al. 2022

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“…The methodology used to solve the BpMD problem is based on a simple but efficient algorithm which has been successfully applied to solve a wide variety of combinatorial optimization problems, the Path Relinking (PR) metaheuristic (see Resende et al (2010); Pérez-Peló et al (2020); Campos et al (2014) for relevant research in PR). This metaheuristic was first proposed in the framework of tabu search to integrate intensification and diversification strategies (Glover & Laguna, 1998).…”

Section: Algorithmic Proposalmentioning

confidence: 99%

A Reactive Path Relinking algorithm for solving the Bi-objective p-Median and p-Dispersion problem

Lozano-Osorio

Sánchez‐Oro

López-Sánchez

et al. 2022

Preprint

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This paper deals with an interesting facility location problem known as the Bi-objective p -Median and p -Dispersion problem ( BpMD problem). The BpMD problem seeks to locate p facilities to service a set of n demand points and the goal is to minimize the total distance between facilities and demand points and, simultaneously, maximize the minimum distance between all pairs of hosted facilities. The problem is addressed with a novel Path Relinking approach, called Reactive Path Relinking, which hybridizes two of the most extended Path Relinking variants: Interior Path Relinking and Exterior Path Relinking. Additionally, the proposal is adapted to a multi-objective perspective for finding a good approximation of the Pareto front. Computational results prove the superiority of the proposed algorithm over the best procedures found in the

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“…The GRASP was originally presented in [37], but was not formally defined until [38]. It is a very extended metaheuristic that has been successfully applied to many hard optimization problems from a wide variety of areas [39][40][41]. The GRASP follows a multi-start strategy conformed with two different stages: construction and improvement.…”

Section: Algorithmic Proposalmentioning

confidence: 99%

Multi-Objective GRASP for Maximizing Diversity

et al. 2021

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This work presents a novel greedy randomized adaptive search procedure approach for dealing with the maximum diversity problem from a multi-objective perspective. In particular, five of the most extended diversity metrics were considered, with the aim of maximizing all of them simultaneously. The metrics considered have been proven to be in conflict, i.e., it is not possible to optimize one metric without deteriorating another one. Therefore, this results in a multi-objective optimization problem where a set of efficient solutions that are diverse with respect to all the metrics at the same time must be obtained. A novel adaptation of the well-known greedy randomized adaptive search procedure, which has been traditionally used for single-objective optimization, was proposed. Two new constructive procedures are presented to generate a set of efficient solutions. Then, the improvement phase of the proposed algorithm consists of a new efficient local search procedure based on an exchange neighborhood structure that follows a first improvement approach. An effective exploration of the exchange neighborhood structure is also presented, to firstly explore the most promising ones. This feature allowed the local search proposed to limit the size of the neighborhood explored, resulting in an efficient exploration of the solution space. The computational experiments showed the merit of the proposed algorithm, when comparing the obtained results with the best previous method in the literature. Additionally, new multi-objective evolutionary algorithms derived from the state-of-the-art were also included in the comparison, to prove the quality of the proposal. Furthermore, the differences found were supported by non-parametric statistical tests.

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Finding weaknesses in networks using Greedy Randomized Adaptive Search Procedure and Path Relinking

Cited by 9 publications

References 21 publications

A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem

A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem

A Reactive Path Relinking algorithm for solving the Bi-objective p-Median and p-Dispersion problem

Multi-Objective GRASP for Maximizing Diversity

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